Histomorphological effects of sodium arsenite on uterus of rats

Background: Arsenic is highly toxic agent and a risk factor for disease and disability. Arsenic is present in drinking water of many developing and developed countries including Pakistan and due to rapid industrialization its quantity in soil and water is increasing day by day.Methods: In an 18 month study in which we took two principal groups, labelled as control group A and experimental group B. The animals of experimental group B were administered 4 µg of sodium arsenite dissolved in 10 ml of distilled water by oral gavage daily for 14 days. The uterus was removed and processed for paraffin embedding and stained with hematoxylin and eosin (H and E). The histological parameters; uterine luminal diameter, height of uterine luminal epithelium, area occupied by epithelial component of uterine glands and the thickness of myometrium were measured and evaluated by civil AutoCAD 2013 software. The data was analyzed statistically with the statistical package for social sciences (SPSS).Results: Histological results showed the degenerative effects. The luminal diameter of uterine horns was reduced in experimental animals. The height of uterine epithelium was reduced. Area occupied by epithelial component of uterine glands was reduced along the reduction in the thickness of myometrium.Conclusions: The histological abnormalities observed in uterus showed that the degenerative effects may be due to oxidative stress produced by the exposure to sodium arsenite. As sodium arsenite produces the oxidative stress by the formation of free radicals and by the denaturation of proteins

Plant Disease Detection and Classification by Deep Learning

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly

Evaluation of lipoxygenase inhibition of Jatropha gossypifolia, a medicinal plant from Pakistan

No abstract is availabl

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes

Effects of Allium sativum in cardiovascular diseases: A Review

Epidemiologic studies show an inverse correlation between garlic consumption and progression of cardiovascular disease. Cardiovascular disease is associated with multiple factors such as raised serum total cholesterol, raised LDL and an increase in LDL oxidation, increased platelet aggregation, hypertension, and smoking. Numerous in vitro studies have confirmed the ability of garlic to reduce these parameters. Thus, garlic has been shown to inhibit enzymes involved in lipid synthesis, decrease platelet aggregation, prevent lipid peroxidation of oxidized erythrocytes and LDL, increase antioxidant status, and inhibit angiotensin-converting enzyme. These findings have also been addressed in clinical trials. The studies point to the fact that garlic reduces cholesterol, inhibits platelet aggregation, reduces blood pressure, and increases antioxidant status. Since 1993, 44% of clinical trials have indicated a reduction in total cholesterol, and the most profound effect has been observed in garlic's ability to reduce the ability of platelets to aggregate. Mixed results have been obtained in the area of blood pressure and oxidative-stress reduction. The findings are limited because very few trials have addressed these issues. The negative results obtained in some clinical trials may also have resulted from usage of different garlic preparations, unknown active constituents and their bioavailability, inadequate randomization, selection of inappropriate subjects, and short duration of trials. This review analyzes in vitro and in vivo studies published since 1993 and concludes that although garlic appears to hold promise in reducing parameters associated with cardiovascular disease, more in-depth and appropriate studies are required. Keywords: Allium sativum, hypercholesterolemia, antioxidants, cardioprotective, HMG-CoA reductase

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset

Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment

In vitro Studies on Anti-diabetic and Anti-ulcer Potentials of Jatropha gossypifolia (Euphorbiaceae)

Purpose: To evaluate α-glucosidase and α-chymotrypsin enzyme inhibitory activity of Jatropha gossypifolia as a probable remedy for the management of diabetes and ulcer.Methods: Different extracts and fractions of the root, leaf and stem bark of the plant were screened for their α-glucosidase and α-chymotrypsin inhibitory activity using standard in vitro inhibition assays. Acarbose and chymostatin were used as positive control, respectively.Results: n-Butanol and ethyl acetate fraction showed maximum enzyme inhibition for α-glucosidase with 67.93 ± 0.66 and 67.67 ± 0.71 % and half maximal concentration (IC50) of 218.47 ± 0.23 and 213.45 ± 0.12 μg/ml, respectively. Dichloromethane and ethyl acetate leaf fractions exhibited maximum α-chymotrypsin inhibition activity of 85.08 ± 0.38 and 83.87 ± 0.70 %, and IC50 of 133.1 ± 0.68 and 134.5 ± 0.12 μg/ml, respectively, Acarbose exhibited enzyme inhibition activity of 92.14 ± 0.38 % with IC50 of 38.24 ± 0.1 μg/ml, while chymostatin exhibited 93.67 ± 0.38 % enzyme inhibition and IC50 of 8.24 ± 0.11 μg/ml.Conclusion: The presence of bioactive secondary metabolities with enzyme-inhibiting activity lends some support for the traditional use of this plant in the management of diabetes and ulcer. However, further investigation of the plant including identification of its active components is required.Keywords: α-Chymotrypsin, α-Glucosidase, Inhibition, Jatropha gossypifolia, Anti-diabetic, Anti-ulce

Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system